Optimization of Modified Hidden Markov Model for Vision-Based Indonesian Sign Languages Recognition

Sign language is one of the most popular language which is used as a communication bridge that depends on hand movement. As it is used worldwide, the variety of sign languages is very large and most of the people doesn’t understand it which makes the communication between deaf and normal people interrupted. This makes sign language recognition popular as it makes people doesn’t need to understand the sign but still understand the meaning of the sign. But sign language itself has many problems such as the possibility of different dataset has the same movement but different meaning, the method used of each dataset could be ineffective in other dataset, and many other else which makes it difficult to be implemented. Other than that, vision-based recognition is not as popular as sensor-based recognition because of the difference in feature accuracy even though it could give more area of improvement. That’s why the aim of this paper is to presents the combination of methods used to recognize vision-based Indonesian Sign Language and enhance the method using optimization technique. The methodology used in this study follows four steps framework of sign language recognition which is datas